Analysis Software Research Articles

Automated Quantification of Tertiary Lymphoid Structures in Human Tumor Samples Using Immunofluorescence and AI-Powered Analysis Pipeline.

The tumor microenvironment (TME) is a complex entity comprising not only tumor cells but also immune, stromal, and endothelial cells. Preclinical and clinical studies indicate that the density, localization, function, and organization of immune infiltration can influence survival probability and treatment response in many cancers. Among these cell organizations, the clustering of T and B cells into tertiary lymphoid structures (TLS) has been associated with favorable clinical outcomes. In this protocol, we propose a protocol for 7-plex immunofluorescence staining for identifying six cell types enriched in TLS in formalin-fixed paraffin-embedded (FFPE) human tumor sections. Additionally, we provide a detailed methodology for quantifying these cell subtypes using the Halo-AI analysis software. This approach will enable a more precise and detailed characterization of TLS in the TME, opening new avenues for understanding their role in anticancer treatment response.

Illegal drugs and socio-economic changes in a new pivotal region: a court judgements perspective

Abstract This study adopts an integrative approach, combining quantitative and quantitative analysis based on Court judgements to uncover trends, dynamics and projections in the illegal drug trade. A quantitative analysis offers insight into drug specialisations. A Computer-Assisted Qualitative Document Analysis Software unveils socio-economic changes in a newly pivotal Italian region for international drug trade. The analysis highlights the intricate complexities of this underground activity and assesses the role of organised crime groups, shifts in criminal activities, and the emergence of complementary profitable crimes. The study offers a novel understanding of the multi-faceted nature of the drug trade, its impact on society, and its connection with other types of crimes, empowering policymakers to make evidence-based decisions on prevention and treatment policies.

A deep learning based method for left ventricular strain measurements: repeatability and accuracy compared to experienced echocardiographers.

Speckle tracking echocardiography (STE) provides quantification of left ventricular (LV) deformation and is useful in the assessment of LV function. STE is increasingly being used clinically, and every effort to simplify and standardize STE is important. Manual outlining of regions of interest (ROIs) is labor intensive and may influence assessment of strain values. We hypothesized that a deep learning (DL) model, trained on clinical echocardiographic exams, can be combined with a readily available echocardiographic analysis software, to automate strain calculation with comparable fidelity to trained cardiologists. Data consisted of still frame echocardiographic images with cardiologist-defined ROIs from 672 clinical echocardiographic exams from a university hospital outpatient clinic. Exams included patients with ischemic heart disease, heart failure, valvular disease, and conduction abnormalities, and some healthy subjects. An EfficientNetB1-based architecture was employed, and different techniques and properties including data set size, data quality, augmentations, and transfer learning were evaluated. DL predicted ROIs were reintroduced into commercially available echocardiographic analysis software to automatically calculate strain values. DL-automated strain calculations had an average absolute difference of 0.75 (95% CI 0.58-0.92) for global longitudinal strain (GLS), and 1.16 (95% CI 1.03-1.29) for single-projection longitudinal strain (LS), compared to operators. A Bland-Altman plot revealed no obvious bias, though there were fewer outliers in the lower average LS ranges. Techniques and data properties yielded no significant increase/decrease in performance. The study demonstrates that DL-assisted, automated strain measurements are feasible, and provide results within interobserver variation. Employing DL in echocardiographic analyses could further facilitate adoption of STE parameters in clinical practice and research, and improve reproducibility.

The Importance of Knowledge of Study Techniques and Their Impact on Academic Performance

Objective: The main objective of the research is to identify the role of knowledge about study techniques and its impact on the academic performance of students of the Academic Unit of Accounting and Administration (UACYA) at the Autonomous University of Nayarit (UAN). With the results obtained, it will be possible to demonstrate the importance of training them in this subject and make proposals for improvement. Achieving benefits for the student, being a conscious and responsible subject of their cognitive processes and the construction of their knowledge. Theoretical Framework: The conceptualization of meaningful learning is presented, from its precursor David Ausubel (1969), in which the individual is the constructor of his own knowledge, relating the concepts to be learned and with the information he already possesses. Achieving this, the guideline is given to obtain self-regulated learning, where learners are able to plan, control and direct their own learning, through metacognitive, motivational and behavioral processes. Supported by the use of strategies of study habits and techniques that allow them to meet their objectives and impacting their academic performance. Method: A non-experimental design was used, since there was no control with the variables under study, and it was also of a transectional correlational type, since the correlations between the factors under study were analyzed. The students analyzed were those who were found in the facilities of the Academic Unit of Accounting and Administration, so the type of sampling was of convenience, achieving a sample of 328 students. To analyze the correlations, the exploratory and confirmatory factor analysis was used, where first a divergent and convergent analysis was performed, then the reliability analysis, and once the instrument was validated, the exploratory factor analysis was performed, which used the AMOS (Analysis of Moment Structures) software for the confirmatory analysis and adequacy of the model. Results and Discussion: When applying the factor analysis, it was found that the variables were correctly grouped in three components, with a result of 56.478% of the total variance, being acceptable for the social studies, subsequently the KMO test was 0.799 and Bartlett's sphericity was less than .001, confirming the analysis. After elimination of items (p3 and p4) in the Confirmatory Factor Analysis, the fit indices complied with the acceptable values of CFI and RMSEA, obtaining a final adjusted and valid model, i.e. three consistent and relevant dimensions for future research. Research Implications: The practical and theoretical implications of this study highlight the importance of seeking strategies to generate in higher level learners the knowledge about study habits and techniques, which will lead them to be able to manage their own knowledge, and therefore fulfill their academic objectives. At the same time to seek to promote further educational research on issues related to meaningful and self-regulated learning. Originality/Value: This study contributes to the literature by demonstrating the importance of understanding the study habits and techniques of higher education students, and how their application in the learning process impacts academic performance. It sets the guideline for higher education institutions to develop in their students the ability to manage their own knowledge.

Understanding Digital Technology Integration in Merchant Marine College: Examining Teacher Digital Competency through TAM Framework

This study examines the relationship between teacher digital competency and technology adoption in maritime vocational education institutions. The research aims to investigate how digital competency influences perceived usefulness, perceived ease of use, behavioral intention, and actual technology use among maritime educators. Using the Technology Acceptance Model (TAM) framework, the study explores the interconnected relationships between these variables in a specialized educational context. Data collection involved an online survey of lecturers and instructors from maritime vocational colleges under the Ministry of Transportation. The study employed structural equation modeling (SEM) using AMOS software for analysis, providing a comprehensive examination of the relationships between variables. Results reveal significant positive relationships between teacher digital competency and technology adoption factors. The findings demonstrate that digital competency strongly influences both perceived usefulness and ease of use of technology, which in turn affects behavioral intention and actual technology implementation. The research contributes to understanding technology adoption in maritime vocational education and provides practical implications for institutions developing digital competency programs. The study suggests that successful technology integration depends on both technical competency development and positive perceptions of technology's utility, supported by adequate institutional infrastructure.

Medication Errors in Psychiatric Hospitals: A Nationwide Real-World Evidence Study in Saudi Arabia

Background: Medication errors are among the most prevalent medical errors and result in significant morbidity and mortality. They pose a major threat to patient safety in psychiatric hospitals. However, the lack of a comprehensive investigation into the root causes of these errors restricts the development of effective corrective measures. Objective: This study aimed to characterize the types of errors, determine the stages of the medication use process, and identify factors associated with errors occurring among government psychiatric hospitals in Saudi Arabia. Methods: This cross-sectional study was conducted from August 2019 to June 2020. All medication error reports submitted to the Ministry of Health General Administration of Pharmaceutical Care database from 18 government psychiatric hospitals in Saudi Arabia were assessed. The database is de-identified and contains information on all medication errors, including patient demographics, medication information, error information, causes of errors, and reporter information. Medication use was categorized as ordering/prescribing, transcribing, dispensing, administration, and monitoring. The present findings represent a descriptive analysis of the data using Statistical Analysis Software (SAS) version 9.4. Result: A total of 23,355 medication error reports were reported to the database during the study period. Among Saudi Arabian cities, Riyadh (n = 8972, 38.4%) had the highest medication error reporting rate, followed by Taif (n = 3705, 15.9%) and Jeddah (n = 2621, 11.2%). Most reported errors were frequently made by physicians (n = 20,284, 86.9%) and were primarily detected by pharmacists (n = 20,974, 89.8%). Approximately half of them (n = 13,150, 56.3%) were classified as “Category B” that occurred, but they did not reach patients. Most medication errors were reported in adults (n = 22,589, 96.7%) and male patients (n = 16,393, 70.2%). Most error reports were detected at the prescription stage (n = 16,481; 70.6%) during the medication-use process. Work overload of the staff (n = 2911, 12.8%) and drug labeling, packaging, and nomenclature (n = 2826, 12.1%) were the most common contributing factors associated with the reported errors. Olanzapine (n = 1650, 7.1%), omeprazole (n = 1350, 5.8%), and quetiapine (n = 1273, 5.5%) were the most common medications associated with medication errors. Conclusions: Preventable medication errors are typical in psychiatric hospitals and may result in severe consequences. Increased efforts are needed to control and minimize prescribing errors and improve reporting in Saudi Arabia.

Video Analysis - An Important Factor in the Preparation and Conduct of the Modern Soccer Game

Video analysis in soccer has evolved significantly in the last decades. In the past, soccer teams had to rely on videotapes to analyze and review the game. However, with the advent of digital technology, video analysis tools have become more sophisticated and affordable. Nowadays, soccer teams can use video analysis software to optimize match preparation and gameplay in real-time.

The Adoption of Sustainable Supply Chain Management Practices on Performance of Agricultural Sectors in Ethiopia

In recent years, there has been a substantial increase in interest from both academic and corporate sectors concerning sustainable supply chain management. The increasing number of published papers focusing on this topic is a signal for this. To contribute further to the development of this field, this paper presents a comprehensive review of the existing literature on the impact of adopting sustainable supply chain management in the agricultural sector in Ethiopia, considering a total of 67 papers published between 2010 and 2023. This literature review aims to examine barriers, internal and external driving forces of the adoption of sustainable supply chain management practices, the impact of adopting sustainable supply chain management practices on the performance and identify constraints and opportunities of sustainable supply chain management practices in Ethiopian agricultural sectors. Focusing on the Ethiopian context, the study provides insights into the link between sustainable supply chain practices and performance outcomes. Additionally, NVIVO, qualitative data analysis software, was utilized to enhance the review process and ensure the systematic literature analysis.

Evaluating the Diagnostic Potential of Four-Dimensional Flow Magnetic Resonance Imaging in Aortic Stenosis Diagnosis: A Systematic Review and Meta-Analysis.

This systematic review and meta-analysis evaluates the potential of four-dimensionalflow magnetic resonance imaging(4DFM) in assessing aortic stenosis (AS) compared to traditional imaging modalities like two-dimensional phase contrast MRI (2D MRI) and transthoracic echocardiography (TTE). AS is a common and severe valvular heart disease, particularly in older adults, requiring accurate diagnosis for proper clinical management. Conventional imaging methods have limitations in capturing complex flow dynamics, prompting the need for advanced modalities like 4DFM. The objectives of the review were to determine whether 4DFM offers superior diagnostic metrics, including peak aortic jet velocity, transvalvular pressure gradients, and aortic valve area (AVA), and to identify potential advantages of 4DFM in overcoming the limitations of traditional modalities. This review included six cohort studies with 285 participants, examining the diagnostic accuracy of 4DFM in terms of peak aortic jet velocity, transvalvular pressure gradients, and aortic valve area (AVA). Studies were selected from MEDLINE (PubMed), Cochrane Library, and Google Scholar databases between December 2010 and October 2024. The study pool was limited by stringent inclusion criteria focusing on cohort studies that directly compared 4DFM with TTE or 2D MRI for AS assessment. The National Institutes of Health Quality Assessment Tool and Cochrane ROBINS-I tool were used to assess bias. Quantitative results showed that 4DFM typically measured higher AVA values than TTE, with a mean difference of 0.48 cm² (95% CI: -0.16 to 1.12). For mean pressure gradients, 4DFM reported slightly higher measurements in individual studies, but pooled results showed no significant difference compared to TTE (mean difference: 3.32 mmHg, 95% CI: -2.30 to 8.93). In terms of peak aortic jet velocity, 4DFM demonstrated a pooled mean difference of -0.18 m/s (95% CI: -0.44 to 0.08) compared to TTE. High heterogeneity was observed across studies (e.g., I² = 97% for peak velocity, I² = 93% for AVA), likely due to differences in patient populations, imaging protocols, and software for data analysis.4DFM demonstrates potential as a complementary imaging tool, particularly in complex AS cases where conventional methods like TTE may provide inconclusive results. Its capacity to capture intricate flow dynamics and deliver high spatial resolution could inform clinical decision-making, potentially influencing practice guidelines to integrate 4DFM as a supplementary tool. Limitations such as high costs, specialized training requirements, and access challenges currently restrict widespread adoption. Limitations of this review include small sample sizes, high heterogeneity, and variability in patient populations and imaging protocols. Despite these challenges, 4DFMI demonstrated superior spatial resolution and complex cardiovascular flow assessment, suggesting it could serve as a valuable complement to TTE for more detailed AS evaluation, particularly in complex cases.Future studies should aim to standardize imaging protocols, incorporate larger and more diverse populations, and conduct cost-benefit analyses to support the integration of 4DFM into clinical practice, potentially shaping future diagnostic guidelines.

Determination of Ki-67 indices in neuroendocrine tumours of the gastrointestinal tract: the past, the present, and the future.

Ki-67 index (Ki-67i) is integral to the grading of many tumours. There remains considerable variability across pathologists in methods used to determine Ki-67i and in their results. Manual counting (or "eyeballing") is widely used, but digital pathology tools such as web-based image analysis and artificial intelligence-assisted cell detection software have become available in daily pathology practice. This study aims to compare the accuracy and efficiency of manual and two digital methods of Ki-67i determination. H&E and Ki-67 immunohistochemical (IHC) slides/images of 12 gastrointestinal neuroendocrine tumours (GI-NETs) were provided to 8 pathologists to evaluate Ki-67i via manual estimation (ME; the "past"), web-based image analysis using cellular segmentation (AI4Path.ca; the "present"), and software-based image analysis with built-in AI algorithms (QuPath; the "future"). Data collected include Ki67i, time expended, total cells counted, and pathologists' confidence level in the reported result. Deviation of Ki-67i from a gold standard result (GS) was analyzed using multiple linear regression, and results were compared via paired t test. Our results found no statistically significant differences in Ki-67i deviation from GS when comparing ME and AI4P methods for all 12 cases. The QP Ki-67i detection accuracy varied significantly. ME was the method with the least time expenditure. Junior pathologists are less confident in ME. Grading consensus was comparable among all three methods. These findings suggest that while digital pathology can confer increased Ki-67i accuracy in some cases of GI-NETs, higher time expenditure and proper hotspot selection may represent barriers to the adoption of digital pathology methods in the future.

Study on the Overseas Reader Reviews of English Translation of Sanguo Yanyi

<p>Reader evaluation is a key step in measuring the effectiveness of literary translations. A thorough investigation of the reception of Chinese classical novels among overseas readers can help us understand Western readers’ acceptance, value judgments, and preferences regarding these works. This study aims to assess the translation quality and value of the English version of <em>Sanguo Yanyi</em> by Yu Sumei. By collecting reader reviews from platforms such as Amazon and Goodreads, we employ KHCoder text analysis software to generate visualizations that analyze reader feedback. The results indicate that several main factors influence reader evaluations of this translation, including translation quality, English language experience, narrative coherence, and overall reading experience. Therefore, Chinese translators should comprehensively consider the needs and reading habits of overseas readers when translating classical Chinese novels.</p>

An Optimized Strategy for the Integration of Photovoltaic Systems and Electric Vehicles into the Real Distribution Grid

The increasing spread of photovoltaic systems for private households (PVs) and electric vehicles (EVs) in order to reduce carbon emissions significantly impacts operation conditions in existing distribution networks. Variable and unpredictable PVs can stress distribution network operation, mainly manifested in voltage violations during the day. On the other hand, variable loads such as EV chargers which have battery storage in their configuration have the ability of storying a surplus energy and, if it is necessary, support a distribution network with energy, commonly known as vehicle-to-grid concept (V2G), to help voltage stability network enhancement. This paper proposes an optimal power flow (OPF)-based model for EV charging to minimize power exchange between the superior-10 kV grid and the observed distribution feeder. The optimization procedure is realized using the co-simulation approach that connects power flow analysis software and optimization method. Three different scenarios are observed and analysed. The first scenario is referred to as a base case without optimization. The second and third scenarios include optimal EV charging and discharging patterns under different constraints. To test the optimization model, a 90-bus unbalanced distribution feeder modelled based on real-life examples is used. The obtained results suggest that this optimization model does not only significantly reduce the power exchange between an external network and the distribution feeder but also improves voltage stability and demand curve in the distribution feeder.

Eyelid Motion Tracking During Blinking Using High-Speed Imaging and Digital Image Correlation.

This study presents a novel technique to measure the motion of the eyelid during blinking. High-speed imaging and digital image correlation (DIC) were employed to monitor the eyelid during blinking in a non-invasive manner. Both spontaneous and reflex blinks were studied. A black liquid eyeliner was used to generate a speckle pattern on the surface of the eyelid. Facet motion captured through a DIC analysis software generated kinematic data for each blink. Calculations using this data set yielded information on the duration of the blink, eyelid displacements, and peak eyelid velocities. A consistent data set quantified the difference between blink types and reinforced the accuracy and repeatability of this DIC analysis method to measure the kinematics of blinking.

Study on Factors to Ensure Education Quality at University of Sciences - Thai Nguyen University

Research and collect feedback from staff and students on the quality of educational factors, thereby making necessary adjustments to help improve and meet the requirements of program quality inspection. Training and checking the quality of educational and training facilities in the University of Sciences - Thai Nguyen University (TNUS), The research was conducted through a survey to get feedback from officials and lecturers on quality assurance factors at the school. Data collected from 240 officials and lecturers in the school year 2023 - 2024, using SPSS 22.0 software for analysis. Research results show that there are 7 factors affecting quality assurance at the University of Sciences - Thai Nguyen University, including 7 main contents (with 29 evaluation criteria). The survey results are synthesized and summarized specifically to provide recommendations that contribute to improving quality assurance conditions and enhancing the quality of educational and training activities of the School.

FAMILY PLANNING AND MENTAL HEALTH IN PRIMARY CARE: NURSES' PERSPECTIVES

Family planning is among the actions in Primary Care that are relevant and impactful in terms of women's quality of life indicators, but the particularities of women with mental disorders are still invisible to health professionals. The study aims to evaluate family planning performed by Primary Care nurses aimed at women with mental disorders. This is a qualitative and cross-sectional study using Alceste Software for content analysis. The results showed that Primary Care nurses who perform family planning have difficulties in managing the program in their routines. When women with mental disorders are highlighted, these difficulties are accentuated, and in many cases, they are not even identified during the care, which remains fragmented in terms of the demand presented, without considering the woman's comprehensiveness. The nurses also pointed out that they feel a lack of matrix support and collaboration from CAPS in implementing the program. We conclude that family planning should have a prominent place in ongoing training and integrate nurses working in Mental Health, to achieve comprehensive care for women, demystifying myths about pathologies and providing quality of life for this population.

Unravelling T-cell dynamics and immune responses in initial and recurrent uveitis.

This study aimed to identify novel serological targets and investigate immune responses in patients with non-infectious uveitis, focusing on differences between initial onset and recurrent episodes. Differential gene expression analysis, immunocyte typing and T-cell receptor (TCR) gene analysis were conducted on RNA-sequenced peripheral blood samples from healthy individuals (n = 6) and non-infectious uveitis patients (n = 12), divided into 6 patients each at initial onset and recurrent stages. Peripheral blood T-cell types were analysed using flow cytometry. Bioinformatics methods included tools for RNA sequencing data processing, CIBERSORT for immune cell type prediction and specialized software for TCR repertoire analysis. Findings indicated that individuals with recurrent uveitis demonstrated a stronger adaptive immune response and a more pronounced immune imbalance compared to those with initial onset. Memory T cells were predominant in recurrent episodes, suggesting their potential role as biomarkers for disease progression. Significant differences in TCR diversity and V(D)J gene usage were observed between the various uveitis groups and healthy controls. Importantly, 38 uveitis-specific TCR sequences showed substantial expansion in the uveitis patients compared to controls. An elevated expansion of these specific TCR sequences was associated with an increased risk of uveitis development. The study highlights the critical role of adaptive immune responses and specific immune cell types in the pathogenesis of recurrent uveitis. Identification of the uveitis-specific TCR repertoire set could provide deeper insights into the disease and facilitate the development of targeted therapies for uveitis patients.

Software and Data Support for Computer Forensic Analysis

Introduction: the paper highlights the role of software and data support for computer forensic analysis carried out at the stage of judicial investigation and preliminary investigation, emphasizing its importance in the era of digitalization. Purpose: disclose the questions that modern technologies of computer-technical expertise are able to answer, and necessary data sets for research. Methods: the methodological framework for the study is based on the methods of consistency and analysis. Results: the paper presents a methodology for auditing computer equipment (CE) and information and communication technologies (ICT) in the context of criminal investigations. It also elucidates the techniques and methods employed in the study of CE and ICT hardware and software. Conclusions: the author analyzes the state of crime related to the use of computer technology in telematics networks and information and communication technologies, identifies ways to commit crimes related to the use of malware and legal software, the functioning of a dynamic system representing the mechanism of these crimes, as well as systemic aspects of the emergence and investigation of threats of this type.

Assessing the Spatial Distribution, Condition, and Intensity of Soil Acidity for Informed Management Decisions in the Semen Aari District of Ari Zone, Southwestern Ethiopia

Soil acidity poses a significant challenge to agricultural productivity in Ethiopia's highlands, particularly affecting the Semen Ari district in the Ari zone. The common practice of applying agricultural lime to mitigate soil acidity is hampered by a lack of detailed information on the extent, severity, and spatial distribution of acidic soils. This study aims to determine how soil acidity varies spatially by identifying and mapping the specific geographic patterns of soil acidity levels in the Semen Ari district. Seventy-one composite soil samples from the 0–20 cm layer were geo-referenced and analyzed. Using statistical analysis and ArcGIS software for spatial interpolation through ordinary kriging, soil pH ranged from 3.29 to 5.68, classifying 99% of the soils as strongly acidic. The root mean squared error (RMSE) of the interpolation was 0.30. Soil pH showed a significant negative correlation with exchangeable acidity but a non-significant negative correlation with organic carbon and total nitrogen. The results highlight the need for targeted soil management strategies, such as appropriate lime application rates and the cultivation of acid-tolerant crops, to enhance crop yields. Further research is recommended to include comprehensive soil property datasets to better understand the factors influencing soil pH variability, thus supporting more precise management of acidic soils in the region. The generated high-resolution soil acidity map serves as a valuable tool for agricultural planning and decision-making.

Advanced design strategies and applications for enhanced higher-order multisegment denatured Pascal curve gears

The existing Pascal curve gears are limited by inflexible pitch curves and constrained transmission ratio changes, which hinders the application in a range of mechanical systems that require more adaptable gear solutions. To address this, a design procedure for higher-order multisegment denatured Pascal curve gear is proposed. A unified mathematical expression for the Pascal curve gear family is derived, enabling the construction of non-circular gears with free-form pitch curves by adjusting key parameters and offering more flexible pitch curve and a wider range of transmission ratios. Compared with elliptical gears and eccentric circular gears, its transmission ratio range can be expanded by up to 35%. Then the transmission characteristics of these gears are analyzed in detail. To validate the design, the visual analysis and design software of the gear is compiled based on Visual Basic, and is verified with the example. The novelty Pascal curve gears is applied to drive the differential velocity vane pump. The displacement, instantaneous flow rate, and pulsation rate of the differential velocity vane pump are calculated. The comparison with the differential pumps driven by eccentric non-circular gears and Fourier non-circular gears shows that when the pitch curves are not concave, the displacement driven by Pascal curve circular gears is the highest (compared to differential pumps driven by eccentric non-circular gears, and Fourier non-circular gears, their flow rates increased by 50.6%, and 175.7%, respectively), and the instantaneous flow pulsation rate of single pump is the smallest.This study shows that the higher-order multisegment denatured Pascal curve gear provides a viable solution for systems requiring flexible transmission mechanisms and improved operational performance.

Handling missing data in longitudinal clinical trials: three examples from the pediatric psychology literature.

Researchers by default tend to choose complex models when analyzing nonindependent response variable data, this may be particularly applicable in the analysis of longitudinal trial data, possibly due to the ability of such models to easily address missing data by default. Both maximum-likelihood (ML) estimation and multiple imputation (MI) are well-known to be acceptable methods for handling missing data, but much of the recently published quantitative literature has addressed questions regarding the research designs and circumstances under which one should be chosen over the other. The purpose of this article is threefold. First, to clearly define the assumptions underlying three common longitudinal trial data analysis models for continuous dependent variable data: repeated measures analysis of covariance (RM-ANCOVA), generalized estimating equation (GEE), and a longitudinal linear mixed model (LLMM). Second, to clarify when ML or MI should be chosen, and to introduce researchers to an easy-to-use, empirically well-validated, and freely available missing data multiple imputation program: BLIMP. Third, to show how missing longitudinal trial data can be handled in the three data analysis models using three popular statistical analysis software packages (SPSS, Stata, and R) while keeping the published quantitative research in mind.